Browsing by Author "Zhang, Yi"
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- ItemBuilding Scholarly Knowledge Bases with Crowdsourcing and Text Mining(Aachen : RWTH, 2020) Stocker, Markus; Zhang, Chengzhi; Mayr, Philipp; Lu, Wei; Zhang, YiFor centuries, scholarly knowledge has been buried in documents. While articles are great to convey the story of scientific work to peers, they make it hard for machines to process scholarly knowledge. The recent proliferation of the scholarly literature and the increasing inability of researchers to digest, reproduce, reuse its content are constant reminders that we urgently need a transformative digitalization of the scholarly literature. Building on the Open Research Knowledge Graph (http://orkg.org) as a concrete research infrastructure, in this talk we present how using crowdsourcing and text mining humans and machines can collaboratively build scholarly knowledge bases, i.e. systems that acquire, curate and publish data, information and knowledge published in the scholarly literature in structured and semantic form. We discuss some key challenges that human and technical infrastructures face as well as the possibilities scholarly knowledge bases enable.
- ItemChemical Imaging of Mixed Metal Oxide Catalysts for Propylene Oxidation: From Model Binary Systems to Complex Multicomponent Systems(Weinheim : Wiley-VCH, 2021) Sprenger, Paul; Stehle, Matthias; Gaur, Abhijeet; Weiß, Jana; Brueckner, Dennis; Zhang, Yi; Garrevoet, Jan; Suuronen, Jussi‐Petteri; Thomann, Michael; Fischer, Achim; Grunwaldt, Jan‐Dierk; Sheppard, Thomas L.Industrially-applied mixed metal oxide catalysts often possess an ensemble of structural components with complementary functions. Characterisation of these hierarchical systems is challenging, particularly moving from binary to quaternary systems. Here a quaternary Bi−Mo−Co−Fe oxide catalyst showing significantly greater activity than binary Bi−Mo oxides for selective propylene oxidation to acrolein was studied with chemical imaging techniques from the microscale to nanoscale. Conventional techniques like XRD and Raman spectroscopy could only distinguish a small number of components. Spatially-resolved characterisation provided a clearer picture of metal oxide phase composition, starting from elemental distribution by SEM-EDX and spatially-resolved mapping of metal oxide components by 2D Raman spectroscopy. This was extended to 3D using multiscale hard X-ray tomography with fluorescence, phase, and diffraction contrast. The identification and co-localisation of phases in 2D and 3D can assist in rationalising catalytic performance during propylene oxidation, based on studies of model, binary, or ternary catalyst systems in literature. This approach is generally applicable and attractive for characterisation of complex mixed metal oxide systems. © 2021 The Authors. ChemCatChem published by Wiley-VCH GmbH
- ItemDetecting Cross-Language Plagiarism using Open Knowledge Graphs(Aachen, Germany : RWTH Aachen, 2021) Stegmüller, Johannes; Bauer-Marquart, Fabian; Meuschke, Norman; Ruas, Terry; Schubotz, Moritz; Gipp, Bela; Zhang, Chengzhi; Mayr, Philipp; Lu, Wie; Zhang, YiIdentifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application toWebscale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA’s performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available.